[Congressional Bills 116th Congress]
[From the U.S. Government Publishing Office]
[S. 2904 Reported in Senate (RS)]

<DOC>





                                                       Calendar No. 580
116th CONGRESS
  2d Session
                                S. 2904

                          [Report No. 116-289]

 To direct the Director of the National Science Foundation to support 
research on the outputs that may be generated by generative adversarial 
networks, otherwise known as deepfakes, and other comparable techniques 
      that may be developed in the future, and for other purposes.


_______________________________________________________________________


                   IN THE SENATE OF THE UNITED STATES

                           November 20, 2019

 Ms. Cortez Masto (for herself and Mr. Moran) introduced the following 
 bill; which was read twice and referred to the Committee on Commerce, 
                      Science, and Transportation

                            November 9, 2020

               Reported by Mr. Wicker, with an amendment
 [Strike out all after the enacting clause and insert the part printed 
                               in italic]

_______________________________________________________________________

                                 A BILL


 
 To direct the Director of the National Science Foundation to support 
research on the outputs that may be generated by generative adversarial 
networks, otherwise known as deepfakes, and other comparable techniques 
      that may be developed in the future, and for other purposes.

    Be it enacted by the Senate and House of Representatives of the 
United States of America in Congress assembled,

<DELETED>SECTION 1. SHORT TITLE.</DELETED>

<DELETED>    This Act may be cited as the ``Identifying Outputs of 
Generative Adversarial Networks Act'' or the ``IOGAN Act''.</DELETED>

<DELETED>SEC. 2. FINDINGS.</DELETED>

<DELETED>    Congress finds the following:</DELETED>
        <DELETED>    (1) Research gaps currently exist on the 
        underlying technology needed to develop tools to identify 
        authentic videos, voice reproduction, or photos from 
        manipulated or synthesized content, including those generated 
        by generative adversarial networks.</DELETED>
        <DELETED>    (2) The National Science Foundation's focus to 
        support research in artificial intelligence through computer 
        and information science and engineering, cognitive science and 
        psychology, economics and game theory, control theory, 
        linguistics, mathematics, and philosophy, is building a better 
        understanding of how new technologies are shaping the society 
        and economy of the United States.</DELETED>
        <DELETED>    (3) The National Science Foundation has identified 
        the ``10 Big Ideas for NSF Future Investment'' including 
        ``Harnessing the Data Revolution'' and the ``Future of Work at 
        the Human-Technology Frontier'', in with artificial 
        intelligence is a critical component.</DELETED>
        <DELETED>    (4) The outputs generated by generative 
        adversarial networks should be included under the umbrella of 
        research described in paragraph (3) given the grave national 
        security and societal impact potential of such 
        networks.</DELETED>
        <DELETED>    (5) Generative adversarial networks are not likely 
        to be utilized as the sole technique of artificial intelligence 
        or machine learning capable of creating credible deepfakes. 
        Other comparable techniques may be developed in the future to 
        produce similar outputs.</DELETED>

<DELETED>SEC. 3. NSF SUPPORT OF RESEARCH ON MANIPULATED OR SYNTHESIZED 
              CONTENT AND INFORMATION SECURITY.</DELETED>

<DELETED>    The Director of the National Science Foundation, in 
consultation with other relevant Federal agencies, shall support merit-
reviewed and competitively awarded research on manipulated or 
synthesized content and information authenticity, which may include--
</DELETED>
        <DELETED>    (1) fundamental research on digital forensic tools 
        or other technologies for verifying the authenticity of 
        information and detection of manipulated or synthesized 
        content, including content generated by generative adversarial 
        networks;</DELETED>
        <DELETED>    (2) fundamental research on technical tools for 
        identifying manipulated or synthesized content, such as 
        watermarking systems for generated media;</DELETED>
        <DELETED>    (3) social and behavioral research related to 
        manipulated or synthesized content, including the ethics of the 
        technology and human engagement with the content;</DELETED>
        <DELETED>    (4) research on public understanding and awareness 
        of manipulated and synthesized content, including research on 
        best practices for educating the public to discern authenticity 
        of digital content; and</DELETED>
        <DELETED>    (5) research awards coordinated with other Federal 
        agencies and programs, including the Networking and Information 
        Technology Research and Development Program, the Defense 
        Advanced Research Projects Agency, and the Intelligence 
        Advanced Research Projects Agency.</DELETED>

<DELETED>SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON GENERATIVE 
              ADVERSARIAL NETWORKS.</DELETED>

<DELETED>    (a) In General.--The Director of the National Institute of 
Standards and Technology shall support research for the development of 
measurements and standards necessary to accelerate the development of 
the technological tools to examine the function and outputs of 
generative adversarial networks or other technologies that synthesize 
or manipulate content.</DELETED>
<DELETED>    (b) Outreach.--The Director of the National Institute of 
Standards and Technology shall conduct outreach--</DELETED>
        <DELETED>    (1) to receive input from private, public, and 
        academic stakeholders on fundamental measurements and standards 
        research necessary to examine the function and outputs of 
        generative adversarial networks; and</DELETED>
        <DELETED>    (2) to consider the feasibility of an ongoing 
        public and private sector engagement to develop voluntary 
        standards for the function and outputs of generative 
        adversarial networks or other technologies that synthesize or 
        manipulate content.</DELETED>

<DELETED>SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PARTNERSHIP TO 
              DETECT MANIPULATED OR SYNTHESIZED CONTENT.</DELETED>

<DELETED>    Not later than 1 year after the date of enactment of this 
Act, the Director of the National Science Foundation and the Director 
of the National Institute of Standards and Technology shall jointly 
submit to the Committee on Science, Space, and Technology of the House 
of Representatives, the Subcommittee on Commerce, Justice, Science, and 
Related Agencies of the Committee on Appropriations of the House of 
Representatives, the Committee on Commerce, Science, and Transportation 
of the Senate, and the Subcommittee on Commerce, Justice, Science, and 
Related Agencies of the Committee on Appropriations of the Senate a 
report containing--</DELETED>
        <DELETED>    (1) the Directors' findings with respect to the 
        feasibility for research opportunities with the private sector, 
        including digital media companies to detect the function and 
        outputs of generative adversarial networks or other 
        technologies that synthesize or manipulate content; 
        and</DELETED>
        <DELETED>    (2) any policy recommendations of the Directors 
        that could facilitate and improve communication and 
        coordination between the private sector, the National Science 
        Foundation, and relevant Federal agencies through the 
        implementation of innovative approaches to detect digital 
        content produced by generative adversarial networks or other 
        technologies that synthesize or manipulate content.</DELETED>

<DELETED>SEC. 6. GENERATIVE ADVERSARIAL NETWORK DEFINED.</DELETED>

<DELETED>    In this Act, the term ``generative adversarial network'' 
means, with respect to artificial intelligence, the machine learning 
process of attempting to cause a generator artificial neural network 
(referred to in this paragraph as the ``generator'') and a 
discriminator artificial neural network (referred to in this paragraph 
as a ``discriminator'') to compete against each other to become more 
accurate in their function and outputs, through which the generator and 
discriminator create a feedback loop, causing the generator to produce 
increasingly higher-quality artificial outputs and the discriminator to 
increasingly improve in detecting such artificial outputs.</DELETED>

SECTION 1. SHORT TITLE.

    This Act may be cited as the ``Identifying Outputs of Generative 
Adversarial Networks Act'' or the ``IOGAN Act''.

SEC. 2. FINDINGS.

    Congress finds the following:
            (1) Gaps currently exist on the underlying research needed 
        to develop tools that detect videos, audio files, or photos 
        that have manipulated or synthesized content, including those 
        generated by generative adversarial networks. Research on 
        digital forensics is also needed to identify, preserve, 
        recover, and analyze the provenance of digital artifacts.
            (2) The National Science Foundation's focus to support 
        research in artificial intelligence through computer and 
        information science and engineering, cognitive science and 
        psychology, economics and game theory, control theory, 
        linguistics, mathematics, and philosophy, is building a better 
        understanding of how new technologies are shaping the society 
        and economy of the United States.
            (3) The National Science Foundation has identified the ``10 
        Big Ideas for NSF Future Investment'' including ``Harnessing 
        the Data Revolution'' and the ``Future of Work at the Human-
        Technology Frontier'', with artificial intelligence is a 
        critical component.
            (4) The outputs generated by generative adversarial 
        networks should be included under the umbrella of research 
        described in paragraph (3) given the grave national security 
        and societal impact potential of such networks.
            (5) Generative adversarial networks are not likely to be 
        utilized as the sole technique of artificial intelligence or 
        machine learning capable of creating credible deepfakes. Other 
        techniques may be developed in the future to produce similar 
        outputs.

SEC. 3. NSF SUPPORT OF RESEARCH ON MANIPULATED OR SYNTHESIZED CONTENT 
              AND INFORMATION SECURITY.

    The Director of the National Science Foundation, in consultation 
with other relevant Federal agencies, shall support merit-reviewed and 
competitively awarded research on manipulated or synthesized content 
and information authenticity, which may include--
            (1) fundamental research on digital forensic tools or other 
        technologies for verifying the authenticity of information and 
        detection of manipulated or synthesized content, including 
        content generated by generative adversarial networks;
            (2) fundamental research on technical tools for identifying 
        manipulated or synthesized content, such as watermarking 
        systems for generated media;
            (3) social and behavioral research related to manipulated 
        or synthesized content, including human engagement with the 
        content;
            (4) research on public understanding and awareness of 
        manipulated and synthesized content, including research on best 
        practices for educating the public to discern authenticity of 
        digital content; and
            (5) research awards coordinated with other federal agencies 
        and programs, including the Defense Advanced Research Projects 
        Agency and the Intelligence Advanced Research Projects Agency, 
        with coordination enabled by the Networking and Information 
        Technology Research and Development Program.

SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON GENERATIVE 
              ADVERSARIAL NETWORKS.

    (a) In General.--The Director of the National Institute of 
Standards and Technology shall support research for the development of 
measurements and standards necessary to accelerate the development of 
the technological tools to examine the function and outputs of 
generative adversarial networks or other technologies that synthesize 
or manipulate content.
    (b) Outreach.--The Director of the National Institute of Standards 
and Technology shall conduct outreach--
            (1) to receive input from private, public, and academic 
        stakeholders on fundamental measurements and standards research 
        necessary to examine the function and outputs of generative 
        adversarial networks; and
            (2) to consider the feasibility of an ongoing public and 
        private sector engagement to develop voluntary standards for 
        the function and outputs of generative adversarial networks or 
        other technologies that synthesize or manipulate content.

SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PARTNERSHIP TO DETECT 
              MANIPULATED OR SYNTHESIZED CONTENT.

    Not later than 1 year after the date of enactment of this Act, the 
Director of the National Science Foundation and the Director of the 
National Institute of Standards and Technology shall jointly submit to 
the Committee on Science, Space, and Technology of the House of 
Representatives, the Subcommittee on Commerce, Justice, Science, and 
Related Agencies of the Committee on Appropriations of the House of 
Representatives, the Committee on Commerce, Science, and Transportation 
of the Senate, and the Subcommittee on Commerce, Justice, Science, and 
Related Agencies of the Committee on Appropriations of the Senate a 
report containing--
            (1) the Directors' findings with respect to the feasibility 
        for research opportunities with the private sector, including 
        digital media companies to detect the function and outputs of 
        generative adversarial networks or other technologies that 
        synthesize or manipulate content; and
            (2) any policy recommendations of the Directors that could 
        facilitate and improve communication and coordination between 
        the private sector, the National Science Foundation, and 
        relevant Federal agencies through the implementation of 
        innovative approaches to detect digital content produced by 
        generative adversarial networks or other technologies that 
        synthesize or manipulate content.

SEC. 6. GENERATIVE ADVERSARIAL NETWORK DEFINED.

     In this Act, the term ``generative adversarial network'' means, 
with respect to artificial intelligence, the machine learning process 
of attempting to cause a generator artificial neural network (referred 
to in this paragraph as the ``generator'' and a discriminator 
artificial neural network (referred to in this paragraph as a 
``discriminator'') to compete against each other to become more 
accurate in their function and outputs, through which the generator and 
discriminator create a feedback loop, causing the generator to produce 
increasingly higher-quality artificial outputs and the discriminator to 
increasingly improve in detecting such artificial outputs.
                                                       Calendar No. 580

116th CONGRESS

  2d Session

                                S. 2904

                          [Report No. 116-289]

_______________________________________________________________________

                                 A BILL

 To direct the Director of the National Science Foundation to support 
research on the outputs that may be generated by generative adversarial 
networks, otherwise known as deepfakes, and other comparable techniques 
      that may be developed in the future, and for other purposes.

_______________________________________________________________________

                            November 9, 2020

                       Reported with an amendment